Human-Computer Interaction

INTERACT 2015: Human-Computer Interaction – INTERACT 2015 pp 255-262 | Cite as

Recognizing Emotions in Human Computer Interaction: Studying Stress Using Skin Conductance

  • Alexandros Liapis
  • Christos Katsanos
  • Dimitris Sotiropoulos
  • Michalis Xenos
  • Nikos Karousos
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9296)

Abstract

This paper reports an experiment for stress recognition in human-computer interaction. Thirty-one healthy participants performed five stressful HCI tasks and their skin conductance signals were monitored. The selected tasks were most frequently listed as stressful by 15 typical computer users who were involved in pre-experiment interviews asking them to identify stressful cases of computer interaction. The collected skin conductance signals were analyzed using seven popular machine learning classifiers. The best stress recognition accuracy was achieved by the cubic support vector machine classifier both per task (on average 90.8 %) and for all tasks (Mean = 98.8 %, SD = 0.6 %). This very high accuracy demonstrates the potentials of using physiological signals for stress recognition in the context of typical HCI tasks. In addition, the results allow us to move on a first integration of the specific stress recognition mechanism in PhysiOBS, a previously-proposed software tool that supports researchers and practitioners in user emotional experience evaluation.

Keywords

Users emotional experience evaluation Physiological data Skin response conductance Physiological signal analysis 

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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Alexandros Liapis
    • 1
  • Christos Katsanos
    • 1
    • 2
  • Dimitris Sotiropoulos
    • 1
  • Michalis Xenos
    • 1
  • Nikos Karousos
    • 1
    • 2
  1. 1.School of Science and TechnologyHellenic Open UniversityPatraGreece
  2. 2.Technological Educational Institute of Western GreecePatraGreece

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